
A Parallel Processing Framework for Big Data Analytics in Smart City ESG Compliance
References
- Adeoye, O. B., Okoye, C. C., Ofodile, O. C., Odeyemi, O., Addy, W. A., & Ajayi-Nifise, A. O. (2024). Artificial Intelligence in ESG investing: Enhancing portfolio management and performance. International Journal of Science and Research Archive, 11(01), 2194–2205. doi:10.30574/ijsra.2024.11.1.0305
- Alavi, A. H., Jiao, P., Buttlar, W. G., & Lajnef, N. (2018). Internet of Things-enabled smart cities: State-of-the-art and future trends. Measurement, 129, 589-606. doi: 10.1016/j.measurement.2018.07.067
- Amdahl, G. M. (1967). Validity of the single processor approach to achieving large-scale computing capabilities. AFIPS Spring Joint Computer Conference, (pp. 483–485).
- Breiman, L. (2001). Random Forests. In L. Breiman, Machine Learning (pp. 5-32). doi: 10.1023/A:1010933404324
- Dietterich, T. (2000). Ensemble Methods in Machine Learning. (Springer, Ed.) Multiple Classifier Systems, 1857, 1-15. doi: 10.1007/3-540-45014-9_1
- Furtunato, A. F., Georgiou, K., Eder, K., & Xavier-de-Souza, S. (2020). When Parallel Speedups Hit the Memory Wall. IEEE Access, 8, 79225-79238. doi:10.1109/ACCESS.2020.2990418
- Gelvez-Almeida, E., Mora, M., Barrientos, R. J., Hernández-García, R., Vilches-Ponce, K., & Vera, M. (2024). A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks. Math. Comput. Appl., 29(3), 40. doi: 10.3390/mca29030040
- Gholami, A., Yao, Z., Kim, S., Hooper, C., Mahoney, M. W., & Keutzer, K. (2024). AI and Memory Wall. IEEE Micro Journal. doi: 10.48550/arXiv.2403.14123
- Halpern, M., Boroujerdian, B., Mummert, T., Duesterwald, E., & Reddi, V. (2019). One Size Does Not Fit All: Quantifying and Exposing the Accuracy-Latency Trade-Off in Machine Learning Cloud Service APIs via Tolerance Tiers. 2019 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS), (pp. 34-47).
- Kourtit, K., Elmlund, P., & Nijkamp, P. (2020). The urban data deluge: challenges for smart urban planning in the third data revolution. International Journal of Urban Sciences, 24(4), 445-461. doi: 10.1080/12265934.2020.1755353
- Oumoulylte, M., Allaoui, A. E., Farhaoui, Y., & Boughrous, A. A. (2025). Efficient Air Quality Prediction Models Based on Supervised Machine Learning Techniques. The 5th Edition of Oriental Days for the Environment “Green Lab. Solution for Sustainable Development” (JOE5). E3S Web Conf. doi: 10.1051/e3sconf/202563202012
- Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 101, 63-80. doi: 10.1016/j.comnet.2015.12.023
- United States Environmental Protection Agency. (2026, March 20). Air Data:Air Quality Data Collected at Outdoor Monitors Across the US. Retrieved from EPA - Data:https://aqs.epa.gov/aqsweb/airdata/download_files.html
DOI: https://doi.org/10.2478/picbe-2026-0182 | Journal eISSN: 2558-9652
Language: English
Page range: 2394 - 2406
Published on: Jul 21, 2026
Published by: Bucharest University of Economic Studies
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Keywords:
Related subjects:
© 2026 Andreea-Mihaela NICULAE, published by Bucharest University of Economic Studies
This work is licensed under the Creative Commons Attribution 4.0 License.